Comprehensive Review and Comparative Analysis of Keras for Deep Learning Applications: A Survey on Face Detection Using Convolutional Neural Networks

Abstract

This paper aims to provide a literature review and comparative analysis focused on the importance of using Python as the primary language for machine learning (ML), deep learning (DL): The incorporation of libraries adapted to different areas is highlighted. One of the most depressing things about using Python in the programming sector is its easy and extensive libraries. Of these few, Keras shines with the majority of its design decisions being made based on the core concepts. Keras allows a number of possibilities for model deployment in a production mode, it supports multiple GPUs properly, and it also supports distributed model training. The design of Keras is very simple to use and can be easily learned due to its usage of Python programming language, making it a good open-source tool to be used in building and testing deep learning (DL) models. This paper focuses on Keras, an open-source deep learning application program interface that runs on Python and is based on TensorFlow but compatible with others such as PyTorch, TensorFlow, CODEEPNEATM, and Pygame. The review goes deeper into the details regarding Keras, including its goals, issues it sought to address, achievements it has made and some lessons drawn from its use.